X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/desynchronisation-controle.git/blobdiff_plain/89be6c60f83873b92ea5cfa0b5aa488a4a5744be..3b53d32cc1a8b4f341064dc7252d44f956a932fc:/exp_controle_asynchrone/simulMWSN.py diff --git a/exp_controle_asynchrone/simulMWSN.py b/exp_controle_asynchrone/simulMWSN.py index 8bbeddb..41e82c6 100644 --- a/exp_controle_asynchrone/simulMWSN.py +++ b/exp_controle_asynchrone/simulMWSN.py @@ -6,17 +6,21 @@ import pylab as pb from itertools import * from scipy import optimize as opt from copy import deepcopy +import sys as sy +import cv as cv +import cv2 as cv2 error = 0.1 epsilon = 1E-10 vrate = 0.8 p = 0.7 coteCarre = 50 -distanceEmmissionMax = 30 +distanceEmmissionMax = 20 nbiter = 1000 POS = 1 POS_NUL = 2 POSINF1 = 3 init = [] +fichier_init="config_initiale_default.txt" @@ -28,6 +32,9 @@ lg = [(0, 1, 22.004323820359151), (0, 2, 28.750632705280324), (0, 3, 29.68069293 #lg= [(0,1,23),(1,0,15),(1,2,45)] sink = n-1 +def distance(d1,d2): + return mt.sqrt(sum([(d1[t]-d2[t])**2 for t in d1])) + def genereGraph(): test = False @@ -42,7 +49,53 @@ def genereGraph(): G.add_edge(io,ie,weight=dist) G.add_edge(ie,io,weight=dist) test = not(any([ not(nx.has_path(G,o,sink)) for o in G.nodes() if sink in G.nodes() and o != sink])) - return G + return (G,l) + + +def afficheGraph(G,l,tx,ty,sink): + r = 20 + img = cv.CreateImage ((tx, ty), 32, 3) + cv.Rectangle(img, (0,0),(tx,ty), cv.Scalar(255,255,255), thickness=-1) + def px((x,y)): + return(int(tx*x/coteCarre),ty-int(ty*y/coteCarre)) + for i in set(range(len(l)))-set([sink]): + (x,y) = l[i] + pix,piy = px((x,y)) + demx = distanceEmmissionMax*tx/coteCarre + cv.Circle(img, (pix,piy),demx, cv.Scalar(125,125,125)) + + for i in set(range(len(l)))-set([sink]): + (x,y) = l[i] + pix,piy = px((x,y)) + cv.Circle(img, (pix,piy),r, cv.Scalar(125,125,125),thickness=-1) + + #sink + (x,y) = l[sink] + pix,piy = px((x,y)) + + cv.Rectangle(img, (pix-r/2,piy-r/2),(pix+r/2,piy+r/2), cv.Scalar(125,125,125), thickness=-1) + + for i in range(len(l)): + for j in range(len(l)): + + if np.linalg.norm(np.array(l[i])-np.array(l[j])) < distanceEmmissionMax : + (xi,yi) = l[i] + pixi,piyi = px((xi,yi)) + (xj,yj) = l[j] + pixj,piyj = px((xj,yj)) + cv.Line(img, (pixi,piyi), (pixj,piyj), cv.Scalar(125,125,125)) + + + """ + for i in range(len(l)): + (x,y) = l[i] + pix,piy = px((x,y)) + print i + textColor = (0, 0, 255) # red + font = cv2.FONT_HERSHEY_SIMPLEX + imgp = + cv2.putText(img, str(i), (pix-r/4,piy-r/2),font, 3.0, textColor)#,thickn """ + cv.SaveImage("SensorNetwork.png",img) G = nx.DiGraph() G.add_weighted_edges_from(lg) @@ -52,6 +105,18 @@ G.add_weighted_edges_from(lg) #nx.draw(G) #pb.show() +(G,l) = genereGraph() +N = G.nodes() +#V = list(set(sample(N,int(len(N)*vrate)))-set([sink])) +V = list(set(N)-set([sink])) +source = V +print "source",source +afficheGraph(G,l,500,500,sink) +#nx.draw(G) +#pb.show() + + + @@ -60,15 +125,8 @@ G.add_weighted_edges_from(lg) #print G.edges(data=True) #TODO afficher le graphe et etre sur qu'il est connexe -N = G.nodes() - - -#V = list(set(sample(N,int(len(N)*vrate)))-set([sink])) -V = list(set(N)-set([sink])) -source = V -print "source",source L = range(len(G.edges())) @@ -103,6 +161,7 @@ alpha = 0.5 beta = 1.3E-8 gamma = 55.54 delta = 0.2 +zeta = 0.1 amplifieur = 1 sigma2 = 3500 Bi = 5 @@ -141,8 +200,6 @@ def aminp(l): -def distance(d1,d2): - return mt.sqrt(sum([(d1[t]-d2[t])**2 for t in d1])) def AfficheVariation (up,vp,lap,wp,thetap,etap,qp,Psp,Rhp,xp,valeurFonctionDualep): @@ -240,7 +297,7 @@ def maj(k,maj_theta,mxg,idxexp): vp = {} for h in V: if not ASYNC or random() < taux_succes: - s = Rh[h]- mt.log(float(sigma2)/D)/(gamma*mt.pow(Ps[h],float(1)/3)) + s = Rh[h]- mt.log(float(sigma2)/D)/(gamma*mt.pow(Ps[h],float(2)/3)) if abs(s) > mxg : print "ds calcul v",abs(s),idxexp mxg = abs(s) @@ -304,14 +361,20 @@ def maj(k,maj_theta,mxg,idxexp): Psp={} #print "maj des des Psh" def f_Ps(psh,h): - #print "ds f_ps",psh, v[h]* mt.log(float(sigma2)/D)/(gamma*((psh**2)**(float(1)/3))) +la[h]*psh - return v[h]* mt.log(float(sigma2)/D)/(gamma*mt.pow(float(2)/3)) +la[h]*psh + #print "ds f_ps",psh, v[h]* mt.log(float(sigma2)/D)/(gamma*((psh**2)**(float(2)/3))) +la[h]*psh + return v[h]* mt.log(float(sigma2)/D)/(gamma*mt.pow(float(2)/3)) +la[h]*psh for h in V: - if not ASYNC or random() < taux_succes: - lah = 0.05 if la[h] == 0 else la[h] - rep = (float(2*v[h]*mt.log(float(sigma2)/D))/mt.pow(3*gamma*lah,float(3)/5)) - Psp[h] = epsilon if rep <= 0 else rep - else : + if not ASYNC or random() < taux_succes: + """ + lah = 0.05 if la[h] == 0 else la[h] + rep = mt.pow(float(2*v[h]*mt.log(float(sigma2)/D))/(3*gamma*lah),float(3)/5) + Psp[h] = epsilon if rep <= 0 else rep + """ + t= float(-3*la[h]+mt.sqrt(9*(la[h]**2)+64*zeta*v[h]*mt.log(float(sigma2)/D)/gamma))/(16*zeta) + #print t + rep = mt.pow(t,float(3)/5) + Psp[h]=rep + else : Psp[h] = Ps[h] @@ -442,7 +505,20 @@ def initialisation(): -def __evalue_maj_theta__(): +def initialisation_(): + global u, v, la, w, theta , q, Ps, Rh, eta, x,init + fd = open(fichier_init,"r") + l= fd.readline() + init_p = eval(l) + print init_p + theta = omega + (q,Ps,Rh,eta,x,u,v,la,w) = tuple([deepcopy(x) for x in init_p]) + init = [deepcopy(q),deepcopy(Ps),deepcopy(Rh),deepcopy(eta), + deepcopy(x),deepcopy(u),deepcopy(v),deepcopy(la),deepcopy(w)] + + + +def __evalue_maj_theta__(nbexp,out=False): global u, v, la, w, theta , q, Ps, Rh, eta, x, valeurFonctionDuale nbexp = 10 res = {} @@ -450,11 +526,16 @@ def __evalue_maj_theta__(): itermax = 100000 def __maj_theta(k): + mem = [] om = omega/(mt.pow(k,0.75)) return om for idxexp in range(nbexp): mxg = 0 - initialisation() + if not(out): + initialisation() + else : + initialisation_() + k = 1 arret = False sm = 0 @@ -468,6 +549,15 @@ def __evalue_maj_theta__(): if k%100 ==0 : print "k:",k,"erreur sur q", errorq, "et q:",q print "maxg=", mxg + mem = [deepcopy(q),deepcopy(Ps),deepcopy(Rh),deepcopy(eta), + deepcopy(x),deepcopy(u),deepcopy(v),deepcopy(la),deepcopy(w)] + if k%4500 == 0 : + print "#########\n",mem,"\#########\n" + if k%4600 == 0 : + print "#########\n",mem,"\#########\n" + + + if smax - sm > 500: print "variation trop grande" print "init" @@ -479,7 +569,7 @@ def __evalue_maj_theta__(): print "nbre d'iteration trop grand" print "init" print init - exit + sy.exit(1) print "###############" print k